Market adoption of wireless LAN (WLAN) technology has exploded, as users from a wide range of backgrounds and vertical industries have brought this technology into their homes, offices, and increasingly into the public air space. This inflection point has highlighted not only the limitations of earlier-generation systems, but the changing role WLAN technology now plays in people's work and lifestyles, across the globe. Indeed, WLANs are rapidly changing from convenience networks to business-critical networks. Increasingly users are depending on WLANs to improve the timeliness and productivity of their communications and applications, and in doing so, require greater visibility, security, management, and performance from their network.
The rapid proliferation of lightweight, portable computing devices and high-speed WLANs has enabled users to remain connected to various network resources, while roaming throughout a building or other physical location. The mobility afforded by WLANs has generated much interest in applications and services that are a function of a mobile user's physical location. Examples of such applications include: printing a document on the nearest printer, locating a mobile user, displaying a map of the immediate surroundings, and guiding a user inside a building. The required or desired granularity of location information varies from one application to another. Indeed, the accuracy required by an application that selects the nearest network printer, or locates a rogue access point, often requires the ability to determine in what room a mobile station is located. Accordingly, much effort has been dedicated to improving the accuracy of wireless node location mechanisms.
The use of radio signals to estimate the location of a wireless device or node is known. For example, a Global Positioning System (GPS) receiver obtains location information by triangulating its position relative to four satellites that transmit radio signals. The GPS receiver estimates the distance between each satellite based on the time it takes for the radio signals to travel from the satellite to the receiver. Signal propagation time is assessed by determining the time shift required to synchronize the pseudo-random signal transmitted by the satellite and the signal received at the GPS receiver. Although triangulation only requires distance measurements from three points, an additional distance measurement from a fourth satellite is used for error correction.
The distance between a wireless transmitter and a receiver can also be estimated based on the strength of the received signal, or more accurately the observed attenuation of the radio signal. Signal attenuation refers to the weakening of a signal over its path of travel due to various factors like terrain, obstructions and environmental conditions. Generally speaking, the magnitude or power of a radio signal weakens as it travels from its source. The attenuation undergone by an electromagnetic wave in transit between a transmitter and a receiver is referred to as path loss. Path loss may be due to many effects such as free-space loss, refraction, reflection, aperture-medium coupling loss, and absorption.
In business enterprise environments, most location-tracking systems are based on RF triangulation or RF fingerprinting techniques. RF triangulation calculates a mobile user's location based upon the detected signal strength of nearby access points (APs). It naturally assumes that signal strength is a function of proximity in computing the distances between the wireless node and the access points. RF fingerprinting, on the other hand, compares a mobile station's view of the network infrastructure (i.e., the strength of signals transmitted by infrastructure access points) with a database that contains an RF physical model of the coverage area. This database is typically populated by either an extensive site survey or an RF prediction model of the coverage area. For example, Bahl et al., “A Software System for Locating Mobile Users: Design, Evaluation, and Lessons,” http://research.microsoft.com/˜bahl/Papers/Pdf/radar.pdf, describes an RF location system (the RADAR system) in a WLAN environment, that allows a mobile station to track its own location relative to access points in a WLAN environment.
The RADAR system relies on a so-called Radio Map, which is a database of locations in a building and the signal strength of the beacons emanating from the access points as observed, or estimated, at those locations. For example, an entry in the Radio Map may look like (x, y, z, ssi (i=1 . . . n)), where (x, y, z) are the physical coordinates of the location where the signal is recorded, and ssi is the signal strength of the beacon signal emanating from the ith access point. According to Bahl et al., Radio Maps may be empirically created based on heuristic evaluations of the signals transmitted by the infrastructure radios at various locations, or mathematically created using a mathematical model of indoor RF signal propagation. To locate the position of the mobile user in real-time, the mobile station measures the signal strength of each access point within range. It then searches a Radio Map database against the detected signal strengths to find the location with the best match. Bahl et al. also describe averaging the detected signal strength samples, and using a tracking history-based algorithm, to improve the accuracy of the location estimate. Bahl et al. also address fluctuations in RF signal propagation by using multiple Radio Maps and choosing the Radio Map which best reflects the current RF environment. Specifically, an access point detects beacon packets from other access points and consults a Radio Map to estimate its location, and evaluates the estimated location with the known location. The RADAR system chooses the Radio Map which best characterizes the current RF environment, based on a sliding window average of received signal strengths.
While the RADAR system allows a mobile station to track its location, it does not disclose a system that allows the WLAN infrastructure to track the location of wireless nodes, such as rogue access points. Indeed, the use of a WLAN infrastructure to collect signal strength information corresponding to a mobile station for use in estimating the location of the mobile station does present certain difficulties. The extremely portable nature of mobile stations renders it important to possess sufficiently recent signal strength information for a given mobile station, as it may have moved to a new location after one or more signal strength measurements have been collected by the location infrastructure. In the RADAR system, this is not an issue since the mobile station computes its own location based on beacon packets that access points regularly transmit as part of the normal access point mode defined by the 802.11 protocol. Accordingly, the mobile station can scan all available channels to obtain one or more beacon packets on the channels, and then compute its location based on the newly detected signal strength. In the reverse situation where the WLAN collects signal strength data from wireless nodes, collecting signal strength data can be problematic, since mobile stations ordinarily do not regularly transmit management frames, such as beacon packets, once they associate with an access point. Moreover, adjacent access points in typical WLAN environments operate on non-overlapping channels to exploit the advantages associated with frequency re-use. Accordingly, access points adjacent to the access point to which a given mobile station is associated will not be able to detect RF signals transmitted by the mobile station, unless the adjacent access points go “off channel” to detect the signals transmitted by the mobile station. Switching to an alternate channel to passively or actively scan for a given mobile station interrupts connections with mobile stations associated with an access point. The lack of signal strength information from adjacent access points is especially problematic to wireless node location as signal strength measurements from adjacent access points are typically the most useful in locating a given mobile station. For example, the signal strength information from adjacent access points is typically more accurate as the adjacent access points are generally closer in proximity to the mobile station. Still further, the lack of signal strength information from a sufficient number of access points may prevent the mobile station from being located entirely as location mechanisms require signal strength information from a minimum number of sources.
In light of the foregoing, a need exists in the art for methods, apparatuses and systems directed to refreshing signal strength information in an infrastructure wireless node location mechanism. In addition, a need in the art exists for wireless node location mechanisms that efficiently integrate into WLAN infrastructures. Embodiments of the present invention substantially fulfill these needs.